Heart Segmentation on PA Chest X-Ray Images by Model-Based Deep Learning Approach

Nowadays, Cardivascular Diseases (CVDs) are the most common cause of deaths worldwide. Some lesions connected to CVDs (such as Cardiomegaly, pleural effusion, pulmonary edema) can be detected on acquired Posterior Anterior (PA) chest X-ray projection images (one of the cheapest and most common modality in radiology). By developing robust methods for the segmentation of the heart as a computer aided diagnostic system (CADx) the automatic screening for such lesions becomes possible. In this paper we present a novel model-based deep learning approach by combining Active Shape Models (ASM), Deep Convolutional Neural Networks (CNN) and expert image processing knowledge through numerical optimization solved by Alternating Direction Method of Multipliers (ADMM) based optimization scheme. The proposed method is evaluated on various datasets against a purely neural network-based approach that is architecturally similar to the proposed solution. Results indicate that our model is more robust, provides a better generalization across datasets especially when segmentation is challenging due to lesions obscuring the target area. Comparison is done on the JSRT dataset as well as on a private dataset containing samples acquired in daily medical practice. Quantitatively, the proposed method achieved a Dices Score of
$0.877\pm 0.05$
and a Hausdorff distance of 17.35 mm
$\pm 4.52$
mm,